328 citations · 401 across the 17 of their papers we have counts for
17 papers
Lamps: Learning Anatomy from Multiple Perspectives via Self-supervision in Chest Radiographs
Ziyu Zhou, Haozhe Luo, Mohammad Reza Hosseinzadeh Taher +4
Foundation models have been successful in natural language processing and computer vision because they are capable of capturing the underlying structures (foundation) of natural la…
DermINO: Hybrid Pretraining for a Versatile Dermatology Foundation Model
Jingkai Xu, De Cheng, Xiangqian Zhao +27
Skin diseases impose a substantial burden on global healthcare systems, driven by their high prevalence (affecting up to 70% of the population), complex diagnostic processes, and a…
Foundation X: Integrating Classification, Localization, and Segmentation through Lock-Release Pretraining Strategy for Chest X-ray Analysis
Nahid Ul Islam, DongAo Ma, Jiaxuan Pang +3
Developing robust and versatile deep-learning models is essential for enhancing diagnostic accuracy and guiding clinical interventions in medical imaging, but it requires a large a…
ACE: Anatomically Consistent Embeddings in Composition and Decomposition
Ziyu Zhou, Haozhe Luo, Mohammad Reza Hosseinzadeh Taher +4
Medical images acquired from standardized protocols show consistent macroscopic or microscopic anatomical structures, and these structures consist of composable/decomposable organs…
Representing Part-Whole Hierarchies in Foundation Models by Learning Localizability, Composability, and Decomposability from Anatomy via Self-Supervision
Mohammad Reza Hosseinzadeh Taher, Michael B. Gotway, Jianming Liang
Humans effortlessly interpret images by parsing them into part-whole hierarchies; deep learning excels in learning multi-level feature spaces, but they often lack explicit coding o…
Learning Anatomically Consistent Embedding for Chest Radiography
Ziyu Zhou, Haozhe Luo, Jiaxuan Pang +3
Self-supervised learning (SSL) approaches have recently shown substantial success in learning visual representations from unannotated images. Compared with photographic images, med…
Foundation Ark: Accruing and Reusing Knowledge for Superior and Robust Performance
DongAo Ma, Jiaxuan Pang, Michael B. Gotway +1
Deep learning nowadays offers expert-level and sometimes even super-expert-level performance, but achieving such performance demands massive annotated data for training (e.g., Goog…
Towards Foundation Models Learned from Anatomy in Medical Imaging via Self-Supervision
Mohammad Reza Hosseinzadeh Taher, Michael B. Gotway, Jianming Liang
Human anatomy is the foundation of medical imaging and boasts one striking characteristic: its hierarchy in nature, exhibiting two intrinsic properties: (1) locality: each anatomic…
Large-batch Optimization for Dense Visual Predictions
Zeyue Xue, Jianming Liang, Guanglu Song +4
Training a large-scale deep neural network in a large-scale dataset is challenging and time-consuming. The recent breakthrough of large-batch optimization is a promising way to tac…
Unifying Visual Perception by Dispersible Points Learning
Jianming Liang, Guanglu Song, Biao Leng +1
We present a conceptually simple, flexible, and universal visual perception head for variant visual tasks, e.g., classification, object detection, instance segmentation and pose es…
DiRA: Discriminative, Restorative, and Adversarial Learning for Self-supervised Medical Image Analysis
Fatemeh Haghighi, Mohammad Reza Hosseinzadeh Taher, Michael B. Gotway +1
Discriminative learning, restorative learning, and adversarial learning have proven beneficial for self-supervised learning schemes in computer vision and medical imaging. Existing…
CAiD: Context-Aware Instance Discrimination for Self-supervised Learning in Medical Imaging
Mohammad Reza Hosseinzadeh Taher, Fatemeh Haghighi, Michael B. Gotway +1
Recently, self-supervised instance discrimination methods have achieved significant success in learning visual representations from unlabeled photographic images. However, given th…
Seeking an Optimal Approach for Computer-Aided Pulmonary Embolism Detection
Nahid Ul Islam, Shiv Gehlot, Zongwei Zhou +2
Pulmonary embolism (PE) represents a thrombus ("blood clot"), usually originating from a lower extremity vein, that travels to the blood vessels in the lung, causing vascular obstr…
A Systematic Benchmarking Analysis of Transfer Learning for Medical Image Analysis
Mohammad Reza Hosseinzadeh Taher, Fatemeh Haghighi, Ruibin Feng +2
Transfer learning from supervised ImageNet models has been frequently used in medical image analysis. Yet, no large-scale evaluation has been conducted to benchmark the efficacy of…
Transferable Visual Words: Exploiting the Semantics of Anatomical Patterns for Self-supervised Learning
Fatemeh Haghighi, Mohammad Reza Hosseinzadeh Taher, Zongwei Zhou +2
This paper introduces a new concept called "transferable visual words" (TransVW), aiming to achieve annotation efficiency for deep learning in medical image analysis. Medical imagi…
Learning Semantics-enriched Representation via Self-discovery, Self-classification, and Self-restoration
Fatemeh Haghighi, Mohammad Reza Hosseinzadeh Taher, Zongwei Zhou +2
Medical images are naturally associated with rich semantics about the human anatomy, reflected in an abundance of recurring anatomical patterns, offering unique potential to foster…
Models Genesis
Zongwei Zhou, Vatsal Sodha, Jiaxuan Pang +2
Transfer learning from natural images to medical images has been established as one of the most practical paradigms in deep learning for medical image analysis. To fit this paradig…